AutoML Leaderboard
AutoML Performance

AutoML Performance Boxplot

Features Importance

Spearman Correlation of Models

Summary of 1_Baseline
<< Go back
Baseline Classifier (Baseline)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
average_precision
Training time
1.1 seconds
Metric details
|
score |
threshold |
| logloss |
0.693177 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.67052 |
0.448688 |
| accuracy |
0.504348 |
0.448688 |
| precision |
0.504348 |
0.448688 |
| recall |
1 |
0.448688 |
| mcc |
0 |
0.448688 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.693177 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.67052 |
0.448688 |
| accuracy |
0.504348 |
0.448688 |
| precision |
0.504348 |
0.448688 |
| recall |
1 |
0.448688 |
| mcc |
0 |
0.448688 |
Confusion matrix (at threshold=0.448688)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
57 |
| Labeled as 1 |
0 |
58 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back
Summary of 2_DecisionTree
<< Go back
Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
average_precision
Training time
2.5 seconds
Metric details
|
score |
threshold |
| logloss |
0.31362 |
nan |
| auc |
0.924682 |
nan |
| f1 |
0.877193 |
0.393939 |
| accuracy |
0.878261 |
0.393939 |
| precision |
1 |
0.85 |
| recall |
1 |
0.0782609 |
| mcc |
0.757035 |
0.393939 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.31362 |
nan |
| auc |
0.924682 |
nan |
| f1 |
0.877193 |
0.393939 |
| accuracy |
0.878261 |
0.393939 |
| precision |
0.892857 |
0.393939 |
| recall |
0.862069 |
0.393939 |
| mcc |
0.757035 |
0.393939 |
Confusion matrix (at threshold=0.393939)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
51 |
6 |
| Labeled as 1 |
8 |
50 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back
Summary of 3_Linear
<< Go back
Logistic Regression (Linear)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
average_precision
Training time
3.1 seconds
Metric details
|
score |
threshold |
| logloss |
0.163214 |
nan |
| auc |
0.980944 |
nan |
| f1 |
0.948276 |
0.41277 |
| accuracy |
0.947826 |
0.41277 |
| precision |
1 |
0.568079 |
| recall |
1 |
0.00812168 |
| mcc |
0.900647 |
0.568079 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.163214 |
nan |
| auc |
0.980944 |
nan |
| f1 |
0.948276 |
0.41277 |
| accuracy |
0.947826 |
0.41277 |
| precision |
0.948276 |
0.41277 |
| recall |
0.948276 |
0.41277 |
| mcc |
0.895644 |
0.41277 |
Confusion matrix (at threshold=0.41277)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
54 |
3 |
| Labeled as 1 |
3 |
55 |
Learning curves

Coefficients
| feature |
Learner_1 |
| PEER_PRESSURE |
1.90678 |
| YELLOW_FINGERS |
1.73776 |
| CHRONIC DISEASE |
1.69633 |
| ALLERGY |
1.68397 |
| SWALLOWING DIFFICULTY |
1.65569 |
| COUGHING |
1.60986 |
| ALCOHOL CONSUMING |
1.59961 |
| FATIGUE |
1.27991 |
| WHEEZING |
1.19506 |
| ANXIETY |
0.865616 |
| SMOKING |
0.36864 |
| CHEST PAIN |
0.244263 |
| SHORTNESS OF BREATH |
0.108942 |
| AGE |
0.10044 |
| GENDER |
-0.0231852 |
| intercept |
-4.98493 |
Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
Top-10 Worst decisions for class 0 (Fold 1)

Top-10 Best decisions for class 0 (Fold 1)

Top-10 Worst decisions for class 1 (Fold 1)

Top-10 Best decisions for class 1 (Fold 1)

<< Go back
Summary of 4_Default_Xgboost
<< Go back
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: average_precision
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
average_precision
Training time
3.7 seconds
Metric details
|
score |
threshold |
| logloss |
0.144457 |
nan |
| auc |
1 |
nan |
| f1 |
1 |
0.296689 |
| accuracy |
1 |
0.296689 |
| precision |
1 |
0.296689 |
| recall |
1 |
0.0426503 |
| mcc |
1 |
0.296689 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.144457 |
nan |
| auc |
1 |
nan |
| f1 |
1 |
0.296689 |
| accuracy |
1 |
0.296689 |
| precision |
1 |
0.296689 |
| recall |
1 |
0.296689 |
| mcc |
1 |
0.296689 |
Confusion matrix (at threshold=0.296689)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
57 |
0 |
| Labeled as 1 |
0 |
58 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots
Dependence (Fold 1)

SHAP Decision plots
Top-10 Worst decisions for class 0 (Fold 1)

Top-10 Best decisions for class 0 (Fold 1)

Top-10 Worst decisions for class 1 (Fold 1)

Top-10 Best decisions for class 1 (Fold 1)

<< Go back
Summary of 5_Default_NeuralNetwork
<< Go back
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
average_precision
Training time
2.2 seconds
Metric details
|
score |
threshold |
| logloss |
0.121825 |
nan |
| auc |
0.990926 |
nan |
| f1 |
0.973451 |
0.593571 |
| accuracy |
0.973913 |
0.593571 |
| precision |
1 |
0.593571 |
| recall |
1 |
0.000153975 |
| mcc |
0.949138 |
0.593571 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.121825 |
nan |
| auc |
0.990926 |
nan |
| f1 |
0.973451 |
0.593571 |
| accuracy |
0.973913 |
0.593571 |
| precision |
1 |
0.593571 |
| recall |
0.948276 |
0.593571 |
| mcc |
0.949138 |
0.593571 |
Confusion matrix (at threshold=0.593571)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
57 |
0 |
| Labeled as 1 |
3 |
55 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back
Summary of 6_Default_RandomForest
<< Go back
Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: average_precision
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
average_precision
Training time
2.9 seconds
Metric details
|
score |
threshold |
| logloss |
0.203226 |
nan |
| auc |
0.984876 |
nan |
| f1 |
0.945455 |
0.585946 |
| accuracy |
0.947826 |
0.585946 |
| precision |
1 |
0.585946 |
| recall |
1 |
0.0241343 |
| mcc |
0.900647 |
0.585946 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.203226 |
nan |
| auc |
0.984876 |
nan |
| f1 |
0.945455 |
0.585946 |
| accuracy |
0.947826 |
0.585946 |
| precision |
1 |
0.585946 |
| recall |
0.896552 |
0.585946 |
| mcc |
0.900647 |
0.585946 |
Confusion matrix (at threshold=0.585946)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
57 |
0 |
| Labeled as 1 |
6 |
52 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back
Summary of Ensemble
<< Go back
Ensemble structure
| Model |
Weight |
| 4_Default_Xgboost |
1 |
Metric details
|
score |
threshold |
| logloss |
0.144457 |
nan |
| auc |
1 |
nan |
| f1 |
1 |
0.296689 |
| accuracy |
1 |
0.296689 |
| precision |
1 |
0.296689 |
| recall |
1 |
0.0426503 |
| mcc |
1 |
0.296689 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.144457 |
nan |
| auc |
1 |
nan |
| f1 |
1 |
0.296689 |
| accuracy |
1 |
0.296689 |
| precision |
1 |
0.296689 |
| recall |
1 |
0.296689 |
| mcc |
1 |
0.296689 |
Confusion matrix (at threshold=0.296689)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
57 |
0 |
| Labeled as 1 |
0 |
58 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back